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The vegetation communities and spatial patterns on the Fire Island National Seashore are dynamic as the result of interactions with driving forces such as sand deposition, storm-driven over wash, salt spray, surface water, as well as with human disturbances. We used high spatial resolution QuickBird-2 satellite remote sensing data to map both terrestrial and submerged aquatic vegetation communities of the National Seashore. We adopted a stratified classification and unsupervised classification approach for mapping terrestrial vegetation types. Our classification scheme included detailed terrestrial vegetation types identified by previous vegetation mapping efforts of the National Park Service and three generalized categories of high-density seagrass, low-density seagrass coverages, and unvegetated bottom to map the submerged aquatic vegetation habitats. We used underwater videography, GPS-guided field reference photography, and bathymetric data to support remote sensing image classification and information extraction. This study achieved approximately 82% and 75% overall classification accuracy for the terrestrial and submnerged aquatic vegetations, respectively, and provided an updated vegetation inventory and change analysis for the Northeast Coastal and Barrier Network of the National Park Service. 相似文献
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The vegetation communities and spatial patterns on the Fire Island National Seashore are dynamic as the result of interactions with driving forces such as sand deposition, storm-driven over wash, salt spray, surface water, as well as with human disturbances. We used high spatial resolution QuickBird-2 satellite remote sensing data to map both terrestrial and submerged aquatic vegetation communities of the National Seashore. We adopted a stratified classification and unsupervised classification approach for mapping terrestrial vegetation types. Our classification scheme included detailed terrestrial vegetation types identified by previous vegetation mapping efforts of the National Park Service and three generalized categories of high-density seagrass, low-density seagrass coverages, and unvegetated bottom to map the submerged aquatic vegetation habitats. We used underwater videography, GPS-guided field reference photography, and bathymetric data to support remote sensing image classification and information extraction. This study achieved approximately 82% and 75% overall classification accuracy for the terrestrial and submnerged aquatic vegetations, respectively, and provided an updated vegetation inventory and change analysis for the Northeast Coastal and Barrier Network of the National Park Service. 相似文献
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Ele Vahtm?e Tiit Kutser Jonne Kotta Merli P?rnoja Tiia M?ller Lennart Lennuk 《Oceanology》2012,52(6):803-809
It is known that the structure of benthic macrophyte and invertebrate habitats indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats makes it possible to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes, and describe the processes behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view. Our previous results clearly demonstrated that remote sensing methods can be used to map water depth and distribution of taxonomic groups of benthic algae (e.g., red, green, and brown algae) in the optically complex coastal waters of the Baltic Sea. We have as well shown that benthic habitat mapping should be done at high spatial resolution owing to the small-scale heterogeneity of such habitats in Estonian coastal waters. Here we tested the capability of high spatial resolution hyperspectral airborne image in its application for mapping benthic habitats. A big challenge is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study two benthic habitat classification schemes??broader level and finer level??were defined for the study area. The broader level classes were relatively well classified, but discrimination among the units of the finer classification scheme posed a considerable challenge and required a careful approach. Benthic habitat classification provided the highest accuracy in the case of the Spectral Angle Mapper classification method applied to a radiometrically corrected image. Further processing levels, such as spatial filtering and glint correction, decreased the classification accuracy. 相似文献
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基于深度置信网络(DBN)的赤潮高光谱遥感提取研究 总被引：1，自引：0，他引：1
赤潮是严重的海洋灾害,有效监测赤潮对于保护海洋生态环境具有重要意义。高光谱遥感具有光谱分辨率高、图谱合一等优势,适合于海洋赤潮监测。深度学习是机器学习领域的前沿,为高光谱遥感分类提供了新的思路。深度置信网络(Deep Belief Network,DBN)兼具监督分类与非监督分类的特点,通过构建DBN模型,将DBN应用于赤潮灾害遥感监测中,应用渤海机载高光谱遥感数据开展赤潮分类,以达到提取高光谱图像中赤潮水体范围的目的。通过设置对照实验,对比经典的SVM监督分类方法与ISODATA非监督分类方法,发现DBN模型在相同实验条件下具有更高的分类精度,赤潮遥感提取精度提高了3%～11%。 相似文献
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Impacts of Typhoons Tianying and Dawei on seagrass distribution in Xincun Bay, Hainan Province, China 总被引：1，自引：0，他引：1
More than 50% of the typhoons landing in China have landed on the southeast coast, where they have caused great pressure on the coastal environment. Seagrass, one of the most important constituents of coastal ecosystems, is also greatly affected by typhoons. In order to clarify how seagrass distribution variation is affected by typhoons in coastal areas in southeast China, data of Typhoons Dawei and Tianying (category 4 and category 2 respectively, which just ran through the southern part of Hainan province) have been studied. In situ observation and satellite remote sensing data (CBERS-China Brazil Earth Resources Satellite) in 2004 and 2006 were used to retrieve seagrass distribution in Xincun Bay, Hainan province. In situ observations showed that leaf length, stem biomass and above ground biomass on average showed evidence of reduction after Typhoons Tianying and Dawei. However, seagrass density showed no evidence of reduction after typhoon Tianying and Dawei passed by and increased rapidly in January 2006. From results of satellite remote sensing data, seagrass distribution can be detected with high accuracy, and the area of seagrass distribution on the south coast of Xincun Bay in 2006 after the typhoon passed by was smaller than that in 2004 in region A and B. However, in region C, area of seagrass coverage under 20% increased. These results demonstrated that typhoons Tianying and Dawei damaged seagrass bed and helped seagrass to get rid of aged and dead leaves, and this correspondingly facilitated seagrass growth. 相似文献
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近些年来随着生境的破坏其分布面积急遽下降,较大面积连续分布的海草床已经非常少见。本研究首次将声呐探测技术应用到海草床时空分布特征的相关研究中,在河北唐山乐亭-曹妃甸沿海发现目前中国面积最大的鳗草(Zostera marina)海草床,海草床面积高达29.17 km~2,绘制了鳗草分布图和水深分布图,并对鳗草生物量和茎枝高度等基本生物学指标进行了调查。该发现极大丰富了中国海草数据库,并为鳗草的深入研究及保护提供了得天独厚的实验基地,也为周边海洋牧场的构建打下良好的基础。 相似文献
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2002年5月15日，中国成功地发射了第一颗海洋探测卫星——HY-1卫星。该卫星有效载荷中的CCD相机可以获得海陆相互作用区域的高分辨率图像，是我国海岸带地区进行遥感动态监测的一种有效而经济的手段。针对该遥感器建设的海岸带动态监测应用示范系统是国家卫星海洋应用中心HY-1卫星地面应用系统建设的一部分，现已初步投入业务运行。论述了该系统的设计与开发，并通过个例实验验证了该系统的可操作性和实用性，探索了HY-1卫星CCD遥感信息在海岸带监测中的应用潜力，也为类似系统开发起到了示范作用。 相似文献
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海岸线具有重要的生态功能和资源价值,在海洋保护和开发利用管理中具有举足轻重的作用。海岸线精细化、高精度、高频次监测已成为当前海岸线管理工作的新要求和新任务。以宁波市大陆海岸线为研究对象,综合运用多时相卫星遥感影像、航空遥感影像、无人机航摄影像、全野外调查等多源数据,开展宁波市2019-2021年大陆海岸线变化监测研究,对海岸线变化情况、分布情况、使用情况以及多源数据应用情况等进行分析,提出变化岸段分类标准,发现基于多源数据采集的海岸线监测具有覆盖广、准确性高、时效性强、投入成本低等特点,能够为加强海岸线保护与利用管理提供参考。 相似文献
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海草床是三大典型的近海海洋生态系统之一,具有极高的生态服务功能。然而,截至2015年我国近海海草资源分布现状尚不明晰,严重制约了我国海草床保护与修复工作的开展。2015～2021年,笔者通过实地调查,借助船只走航、声呐探测、遥感等技术手段,重点对我国近海海草资源的分布面积、种类及主要威胁进行了全面普查,并据此提出我国海草床管理与可持续利用对策。结果表明,我国近海海域海草床面积共为26 495.69hm2,可划分为:温带海域海草分布区和热带-亚热带海域海草分布区;我国现有海草4科9属16种。其中,温带海域海草床面积为17 095.01 hm2,主要分布在辽宁、河北、天津和山东沿海,分布有2科3属5种,以鳗草(Zosteramarina)和日本鳗草(Z.japonica)为优势种,其中唐山乐亭-曹妃甸海草床面积达9 025.56 hm2,是我国面积最大的海草床;热带-亚热带海域海草床面积为9 400.68 hm2,主要分布在福建、广东、广西和海南沿海,分布有4科8属12种,以泰来草(Thalassia hemprichii)、海菖蒲(Enhalus acoroides... 相似文献
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赤潮是最严重的海洋灾害之一,它不仅破坏海洋渔业生产、恶化海洋环境、影响滨海旅游业,而且还会影响人类健康。卫星遥感技术具有覆盖范围广、重复率高、成本低廉等优势,近年来已成为赤潮监测不可或缺的重要手段。本研究利用C++语言建立了一套赤潮遥感监测系统。该系统能自动接收和处理遥感数据,并利用赤潮水体的光谱和固有光学量特征自动提取赤潮信息。在实际业务化应用中,该系统被国家监测部门采用,在2013年4月至9月东海赤潮高发期间,该系统制作了55期赤潮遥感监测产品,用于指导船舶现场监测工作,取得了良好的应用效果。利用本系统对近年来东海发生的27次大型赤潮事件进行了发生位置和面积的提取,并与现场观测结果进行比较。结果显示,系统对大部分赤潮范围的识别有较好的效果,对赤潮识别准确度大概在80%左右。 相似文献
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海草生长在热带、亚热带和温带海洋水域, 是全球海洋生态系统中重要的结构和功能组成部分, 具有极高的生态服务价值。由于受到人类活动和自然灾害的强烈影响, 海草正在全球范围内快速衰退, 因此开展海草资源监测与保护具有非常重要的意义。随着传感器技术的发展, 光学遥感在海草监测中表现出卓越优势。本文综述了国内外海草遥感监测研究现状, 重点总结了研究内容、技术和方法, 论证了海草光学遥感监测的可行性, 旨在为我国海草光学遥感监测厘清适宜数据和可靠技术方法, 并指明潜在研究方向。同时也希望能为海草遥感监测提供借鉴, 并拓展海草资源科学管理的思路。 相似文献
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海洋溢油事故发生后,及时准确地探测油污分布情况极为重要。无人机载多光谱探测海面油膜是高效的溢油监测手段之一,它能弥补卫星遥感的滞后性、精度低等不足,具有灵活性高、实时性强等优点。本研究研制了无人机载多光谱油污探测系统并进行了水面油膜机载探测实验,分别利用最大似然法、最小距离法以及光谱角填图法对成像结果进行分类,并将3种方法的分类结果进行精度评价,结果表明光谱角填图法总分类精度高于90%。利用该系统在小型渔港进行了海面溢油探测实验,基于光谱角填图法将原始图像进行分类。实验结果表明,该溢油探测方法是一种低成本、操作简单、分类精度高的非接触监测手段,能够满足目前海面溢油快速监控的需求,具有良好的应用前景。 相似文献
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为提高遥感影像融合质量,提升资源一号(ZY-1 02D)高光谱遥感影像滨海湿地植被分类精度,提出将ZY-1 02D高光谱影像与空间分辨率为10 m的哨兵2号(Sentinel-2)影像进行Brovey融合,并通过搭建AlexNet卷积神经网络对ZY-1 02D高光谱影像和Brovey融合影像的滨海湿地植被进行分类,与支持向量机、随机森林和BP神经网络分类算法进行精度对比。研究结果表明:经Brovey融合后,AlexNet、支持向量机、随机森林和BP神经网络算法的植被分类总体精度分别提高15.60%、7.00%、14.80%和10.00%,Kappa系数提高了21.35%、9.93%、18.97%、12.85%;基于Brovey影像融合与AlexNet算法的植被分类精度最高,总体精度为92.40%,Kappa系数为89.42%。空谱融合配合AlexNet卷积神经网络有效解决了高光谱遥感影像在滨海湿地植被分类应用中精度较低的问题,为滨海湿地植被资源动态监测提供技术和方法支撑。 相似文献
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纹理特征提取及辅助遥感影像分类技术研究 总被引：6，自引：0，他引：6
研究了利用灰度共生矩阵提取纹理特征的方法，并对利用纹理特征影像辅助光谱特征分类的方法进行了研究。实验结果表明，纹理特征辅助光谱特征分类能够提高遥感影像分类的准确性和精度。 相似文献
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海草是一类生活在近海区域的高等被子植物,初级生产力高,是全球重要的碳汇之一,具有净化水质、消浪固滩,以及为珍稀濒危海洋生物提供栖息地和保护地的功能。无节制的人类活动和频发的极端气象灾害导致近岸海洋污染加剧、海洋生态系统退化和海草床大面积消失。本文以相关海草床修复生态学基础理论为出发点,在自然因素和人为因素两个方面归纳了海草修复需要考虑的因素,梳理了国内外海草修复实践和技术理论方法,提出了利用营养物质、植物激素和物种共生关系等人为促进海草床生长的技术方法,并从法律角度、技术体系、保护体系和科普宣传等方面提出了促进我国海草床修复的建议,以期为我国海草床保护与修复工作提供理论基础和技术参考。 相似文献